Fundamental study toward bridge detection using deep learning based on the aerial photograph and geospatial information

Abstract

Some owner-unknown bridges, which still exist on rivers across Japan, cause accidents involving users due to their defects. In addition, there are concerns over failure because of aging degradation and disaster. Since the total extension of the river is enormous, some municipalities are hesitant to survey the actual situation of the owner-unknown bridge in terms of the workforce and budget. High-resolution aerial photographs and geospatial information, which are readily available at the moment, would be helpful to detect bridges directly by using deep learning and to predict the bridge location as a ground object dividing the river or the intersection between the river and the road. Accordingly, owner-unknown bridges can be specified automatically by comparing the position information of the detected bridge or a ground object with the database. This study investigated detection accuracies of the river, road, and bridge based on the aerial photograph, geospatial information and the image superposed the geospatial information on the aerial photograph.